The Quasi-experimental Design Approach to Causality and the Credibility Revolution in Empirical Economics: A Review of Main Contributions by 2021 Nobel Economics Laureates

Abstract

How to evaluate the causal relationship between economic variables credibly using observational data is a common problem faced by empirical economists. The Royal Swedish Academy of Sciences awarded the 2021 Nobel Prize in economics to David Card for his empirical contributions to labor economics, and Joshua Angrist and Guido Imbens for their methodological contributions to the analysis of causal relationships. This paper reviews how they devise quasi-experimental designs for causality evaluation with the help of natural experiments and the credibility revolution in empirical labor economics with emphasis on the analysis of century-old questions: the employment effects of minimum wage, the labor market impact of immigration and educational investments. The main conclusions are as follows: Firstly, various seemingly different quasi-experimental designs of causality evaluation have the same basic logic. They all use the scene of natural experiments to imitate the way of randomized controlled trials (RCTs), and look for research objects with the same or similar characteristics as the control group that constitutes the counterfactual result of causality evaluation. Moreover, a survey of the classical literature of empirical labor economics shows that the credibility of causality evaluation does not depend on the data and econometric methods themselves, but on the in-depth understanding and thorough analysis of the operation mechanism of social and economic system. Therefore, the in-depth investigation and discussion of natural experiments is essential. Finally, China’s economic reform provides abundant natural experimental resources for quasi-experimental designs. Researchers, who want to tell the Chinese stories with scientific and standardized economic research methods, should pay attention to the in-depth investigation and analysis of the systems behind natural experiments, standardize the quasi-experimental design process to avoid p-hacking, and strengthen the theoretical research of influence mechanism. Policy-makers should pay attention to the limitations and external validity of the research conclusions when learning from quasi-experimental designs to evaluate the effective of policy.